A general class of multivariate skew-elliptical distributions
Journal of Multivariate Analysis
Robust mixture modelling using the t distribution
Statistics and Computing
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
An empirical comparison of EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods
Statistics and Computing
Robust mixture modeling using the skew t distribution
Statistics and Computing
Maximum likelihood estimation for multivariate skew normal mixture models
Journal of Multivariate Analysis
On rates of convergence of efficient detection criteria in signal processing with white noise
IEEE Transactions on Information Theory
Editorial: Special issue on variable selection and robust procedures
Computational Statistics & Data Analysis
A robust EM clustering algorithm for Gaussian mixture models
Pattern Recognition
Multivariate mixture modeling using skew-normal independent distributions
Computational Statistics & Data Analysis
Clustering through empirical likelihood ratio
Computational Statistics & Data Analysis
On mixtures of skew normal and skew $$t$$-distributions
Advances in Data Analysis and Classification
Using conditional independence for parsimonious model-based Gaussian clustering
Statistics and Computing
Finite mixtures of multivariate skew t-distributions: some recent and new results
Statistics and Computing
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A flexible class of probability distributions, convenient for modeling data with skewness behavior, discrepant observations and population heterogeneity is presented. The elements of this family are convex linear combinations of densities that are scale mixtures of skew-normal distributions. An EM-type algorithm for maximum likelihood estimation is developed and the observed information matrix is obtained. These procedures are discussed with emphasis on finite mixtures of skew-normal, skew-t, skew-slash and skew contaminated normal distributions. In order to examine the performance of the proposed methods, some simulation studies are presented to show the advantage of this flexible class in clustering heterogeneous data and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. A real data set is analyzed, illustrating the usefulness of the proposed methodology.